Hebbian learning in neural network models typically increments or decrements
a synaptic weight according to a learning rate and a measure of the activity
correlation between the pre-synaptic (input) and the post-synaptic (output)
neurons [1]}, [2]}, [3]}, [4]}. For the NeuroSwarms controller,
the conceptual similarity of the synaptic strength relation in a neural network
and the physical distance relation in a multi-agent group allows us to construct
a neural activation and learning model for the motion of artificial mobile
agents.
